{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<!--BOOK_INFORMATION-->\n",
    "<a href=\"https://www.packtpub.com/big-data-and-business-intelligence/machine-learning-opencv\" target=\"_blank\"><img align=\"left\" src=\"data/cover.jpg\" style=\"width: 76px; height: 100px; background: white; padding: 1px; border: 1px solid black; margin-right:10px;\"></a>\n",
    "*This notebook contains an excerpt from the book [Machine Learning for OpenCV](https://www.packtpub.com/big-data-and-business-intelligence/machine-learning-opencv) by Michael Beyeler.\n",
    "The code is released under the [MIT license](https://opensource.org/licenses/MIT),\n",
    "and is available on [GitHub](https://github.com/mbeyeler/opencv-machine-learning).*\n",
    "\n",
    "*Note that this excerpt contains only the raw code - the book is rich with additional explanations and illustrations.\n",
    "If you find this content useful, please consider supporting the work by\n",
    "[buying the book](https://www.packtpub.com/big-data-and-business-intelligence/machine-learning-opencv)!*"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<!--NAVIGATION-->\n",
    "< [Using Regression Models to Predict Continuous Outcomes](03.03-Using-Regression-Models-to-Predict-Continuous-Outcomes.ipynb) | [Contents](../README.md) | [Classifying Iris Species Using Logistic Regression](03.05-Classifying-Iris-Species-Using-Logistic-Regression.ipynb) >"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Applying Lasso and Ridge Regression\n",
    "\n",
    "A common problem in machine learning is that an algorithm might work really well on the\n",
    "training set, but when applied to unseen data it makes a lot of mistakes. You can see how\n",
    "this is problematic, since often we are most interested in how a model **generalizes** to new\n",
    "data. Some algorithms (such as decision trees) are more susceptible to this phenomenon\n",
    "than others, but even linear regression can be affected.\n",
    "\n",
    "This phenomeon is also known as **overfitting**, and we will talk about it\n",
    "extensively in [Chapter 5](05.00-Using-Decision-Trees-to-Make-a-Medical-Diagnosis.ipynb), *Using Decision Trees to Make a Medical Diagnosis*,\n",
    "and [Chapter 11](11.00-Selecting-the-Right-Model-with-Hyper-Parameter-Tuning.ipynb), *Selecting the Right Model with Hyperparameter Tuning*.\n",
    "\n",
    "A common technique for reducing overfitting is called **regularization**, which involves\n",
    "adding an additional constraint to the cost function that is independent of all feature values.\n",
    "\n",
    "The two most commonly used **regularizors** are as follows:\n",
    "- **L1 regularization**: This adds a term to the scoring function that is proportional to the sum of all absolute weight values. In other words, it is based on the **L1 norm** of the weight vector (also known as the **rectilinear distance**, **snake distance**, or **Manhattan distance**). The resulting algorithm is also known as **Lasso regression**.\n",
    "- **L2 regularization**: This adds a term to the scoring function that is proportional to the sum of all squared weight values. In other words, it is based on the **L2 norm** of the weight vector (also known as the **Euclidean distance**). Since the L2 norm involves a squaring operation, it punishes strong outliers in the weight vector much harder than the L1 norm. The resulting algorithm is also known as **ridge regression**.\n",
    "\n",
    "The procedure is exactly the same as the preceding one, but we replace the initialization\n",
    "command to load either a Lasso or a RidgeRegression object. Specifically, we have to\n",
    "replace the following command:\n",
    "\n",
    "    In [6]: linreg = linear_model.LinearRegression()\n",
    "\n",
    "For the Lasso regression algorithm, we would change the preceding line of code to the\n",
    "following:\n",
    "\n",
    "    In [6]: lassoreg = linear_model.Lasso()\n",
    "\n",
    "For the ridge regression algorithm, we would change the preceding line of code to the\n",
    "following:\n",
    "\n",
    "    In [6]: ridgereg = linear_model.RidgeRegression()\n",
    "    \n",
    "I encourage you to test these two algorithms on the Boston dataset in place of conventional\n",
    "linear regression. You can use the code below.\n",
    "\n",
    "How does the generalization error (`In [12]`) change? How does the\n",
    "prediction plot (`In [14]`) change? Do you see any improvements in performance?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Loading the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import cv2\n",
    "\n",
    "from sklearn import datasets\n",
    "from sklearn import metrics\n",
    "from sklearn import model_selection\n",
    "from sklearn import linear_model\n",
    "\n",
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "plt.style.use('ggplot')\n",
    "plt.rcParams.update({'font.size': 16})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The Boston dataset is included in Scikit-Learn's example datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "boston = datasets.load_boston()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Inspect the `boston` object:\n",
    "- `DESCR`: Get a description of the data\n",
    "- `data`: The actual data, <`num_samples` x `num_features`>\n",
    "- `feature_names`: The names of the features\n",
    "- `target`: The class labels, <`num_samples` x 1>\n",
    "- `target_names`: The names of the class labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['DESCR', 'data', 'feature_names', 'target']"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dir(boston)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(506, 13)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "boston.data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(506,)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "boston.target.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Training the model\n",
    "\n",
    "This is the line you want to replace with either `Lasso` or `Ridge`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "ridgereg = linear_model.Ridge()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = model_selection.train_test_split(\n",
    "    boston.data, boston.target, test_size=0.1, random_state=42\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Ridge(alpha=1.0, copy_X=True, fit_intercept=True, max_iter=None,\n",
       "   normalize=False, random_state=None, solver='auto', tol=0.001)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ridgereg.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "22.926424500454317"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "metrics.mean_squared_error(y_train, ridgereg.predict(X_train))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.73533535350875179"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ridgereg.score(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Testing the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "y_pred = ridgereg.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "14.785888941249883"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "metrics.mean_squared_error(y_test, y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7f1bf042a080>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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jQ+BU6qEfEzGgkEUuQ1m1ahWKi4vx/fffY+HChRg/fjxOPPFEPPjgg+B5HoDoYvzggw9w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\nQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1bf04380b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 6))\n",
    "plt.plot(y_test, linewidth=3, label='ground truth')\n",
    "plt.plot(y_pred, linewidth=3, label='predicted')\n",
    "plt.legend(loc='best')\n",
    "plt.xlabel('test data points')\n",
    "plt.ylabel('target value')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7f1becafb0f0>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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2kpKSGDJkSLb3mEymfLWn2blzJzt37gRgwYIFuLnpL2ZBVahQQf1XQOq7wlH/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\niAgAC0MiIiIi+gsLQyIiIiICAPw/EZw1+yWj6qAAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1bee31a048>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 6))\n",
    "plt.plot(y_test, y_pred, 'o')\n",
    "plt.plot([-10, 60], [-10, 60], 'k--')\n",
    "plt.axis([-10, 60, -10, 60])\n",
    "plt.xlabel('ground truth')\n",
    "plt.ylabel('predicted')\n",
    "\n",
    "scorestr = r'R$^2$ = %.3f' % ridgereg.score(X_test, y_test)\n",
    "errstr = 'MSE = %.3f' % metrics.mean_squared_error(y_test, y_pred)\n",
    "plt.text(-5, 50, scorestr, fontsize=12)\n",
    "plt.text(-5, 45, errstr, fontsize=12)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<!--NAVIGATION-->\n",
    "< [Using Regression Models to Predict Continuous Outcomes](03.03-Using-Regression-Models-to-Predict-Continuous-Outcomes.ipynb) | [Contents](../README.md) | [Classifying Iris Species Using Logistic Regression](03.05-Classifying-Iris-Species-Using-Logistic-Regression.ipynb) >"
   ]
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}
